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qwen3.8-4b-q4
Qwen3.8-4B is Empero AI's full-parameter distillation of Qwen3.8 2.4T
A95B into the Qwen3.5-4B architecture. It targets mathematics, reasoning,
instruction following, and tool use with a native 262K-token context
window. This default entry uses Q4_K_M weights; a higher-quality Q8_0
build is available as a variant.
Repository: localaiLicense: apache-2.0
qwen3.8-4b-q8
Qwen3.8-4B in the higher-quality Q8_0 GGUF format. This variant preserves
more model fidelity for hosts with enough memory.
Repository: localaiLicense: apache-2.0
parable-qwen3-4b-claude-fable-5
# Parable-Qwen3-4B-Claude-Fable-5
Qwen3 4B fine-tuned on genuine Claude Fable 5 agent traces. Thinking-mode
reasoning, agent/terminal task flavor, tool-call formatting.
Repository: localaiLicense: apache-2.0
qwen3.6-14b-a3b-fablevibes
Qwen3.6-14B-A3B-FableVibes is an Apache-2.0 mixture-of-experts reasoning
model distilled from Fable 5 and Claude Opus traces, with additional tool
calling and coding data. It retains Qwen 3.6 vision support while pruning
the 35B-A3B base to a 14B consumer-oriented footprint. This default entry
uses the recommended Q4_K_M GGUF quantization and its Q8_0 multimodal
projector.
Repository: localaiLicense: apache-2.0
qwen3.6-14b-a3b-fablevibes-q8
Qwen3.6-14B-A3B-FableVibes is an Apache-2.0 mixture-of-experts reasoning
model distilled from Fable 5 and Claude Opus traces, with additional tool
calling and coding data. This entry uses the near-lossless Q8_0 GGUF
quantization and its matching Q8_0 multimodal projector.
Repository: localaiLicense: apache-2.0
agents-a1-4b
Agents-A1-4B is InternScience's Apache-2.0 dense 4B agentic model, based on
Qwen3.5. It is trained for long-horizon search, engineering and scientific
research, instruction following, tool use, and multimodal tasks. This entry
uses the official Q4_K_M GGUF quantization and vision projector.
Repository: localaiLicense: apache-2.0
agents-a1-4b-q8
Agents-A1-4B is InternScience's Apache-2.0 dense 4B agentic model, based on
Qwen3.5. It is trained for long-horizon search, engineering and scientific
research, instruction following, tool use, and multimodal tasks. This entry
uses the official Q8_0 GGUF quantization and vision projector.
Repository: localaiLicense: apache-2.0
qwen3-4b-dflash
Qwen3-4B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. This is the canonical DFlash pairing documented upstream (`z-lab/Qwen3-4B-DFlash` + `Qwen/Qwen3-4B`).
DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3-4B target; the ~0.5 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here.
Flash attention is required for DFlash and is enabled in this config. A GPU is recommended.
License: Apache 2.0 (Qwen3-4B target) / MIT (z-lab DFlash drafter).
Repository: localaiLicense: apache-2.0
qwen3.5-4b-dflash
Qwen3.5-4B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend.
DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3.5-4B target; the ~0.6 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here.
Flash attention is required for DFlash and is enabled in this config. A GPU is recommended.
License: Apache 2.0 (Qwen3.5-4B target) / MIT (z-lab DFlash drafter).
Repository: localaiLicense: apache-2.0
serenity-26b-a4b
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Repository: localaiLicense: apache-2.0
melody1437-26b-a4b-v2.0
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Repository: localaiLicense: apache-2.0
dark-scarlett-v0.3-26b-a4b
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: **E2B**, **E4B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.
Gemma 4 introduces key **capability and architectural advancements**:
* **Reasoning** – All models in the family are designed as highly capable reasoners, with configurable thinking modes.
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Repository: localaiLicense: apache-2.0
gemma-4-26b-a4b-it-qat
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model.
> Four versions of the QAT checkpoints are available:
> * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models.
> * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.
> * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B.
> * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B
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Repository: localaiLicense: apache-2.0
gemma-4-e4b-it-qat-q4_0
Gemma 4 E4B is a multimodal (text + image) instruction-tuned model from Google DeepMind, optimized with Quantization-Aware Training (QAT) to preserve bfloat16-level quality at a fraction of the memory. E4B is a MatFormer "effective 4B" elastic variant, balancing quality and footprint for on-device and edge deployments. This is the official Google Q4_0 GGUF, shipped with its multimodal projector.
License: Apache 2.0 | Authors: Google DeepMind
Repository: localaiLicense: apache-2.0
zero-gemma4-e4b-openzero-q5-k-m
Zero Gemma4-E4B OpenZero is a Gemma 4 E4B instruction fine-tune for
local coding, research, chat, and agentic workflows. It was trained on
2,033 curated OpenZero examples and is distributed as one merged Q5_K_M
GGUF file.
License: OpenZero Community Source v1; see the model repository for terms.
Repository: localaiLicense: other
gemma-4-26b-a4b-it-qat-q4_0
Gemma 4 26B-A4B is a multimodal (text + image) instruction-tuned Mixture-of-Experts model from Google DeepMind, optimized with Quantization-Aware Training (QAT) to preserve bfloat16-level quality at a fraction of the memory. With 26B total parameters and ~4B active per token, it delivers large-model quality at a much lower inference cost. This is the official Google Q4_0 GGUF, shipped with its multimodal projector.
License: Apache 2.0 | Authors: Google DeepMind
Repository: localaiLicense: apache-2.0
gemma-4-e4b-it-qat-mtp
Gemma 4 E4B IT QAT (Google DeepMind) paired with its Multi-Token Prediction (MTP) drafter head for speculative decoding on the llama.cpp backend. The Q4_K_XL target carries the full multimodal (text + image) model; the small `mtp-gemma-4-E4B-it` head predicts several tokens ahead which the target verifies in parallel, accelerating generation with no change to output quality. E4B is a MatFormer "effective 4B" elastic variant, balancing quality and footprint for on-device and edge deployments.
The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. It uses the upstream `gemma4-assistant` architecture registered by llama.cpp PR #23398, so it loads on stock llama.cpp without any patch.
License: Apache 2.0 | Authors: Google DeepMind (target/drafter checkpoints), Unsloth (GGUF conversion)
Repository: localaiLicense: apache-2.0
gemma-4-26b-a4b-it-apex
AI model: gemma-4-26b-a4b-it-apex
Repository: localaiLicense: gemma
gemma-4-26b-a4b-it
Google Gemma 4 26B-A4B-IT is an open-source multimodal Mixture-of-Experts model with 26B total parameters and 4B active parameters. It handles text and image input, generating text output, with a 256K context window and support for 140+ languages. The MoE architecture provides strong performance with efficient inference. Well-suited for question answering, summarization, reasoning, and image understanding tasks.
Repository: localaiLicense: apache-2.0
gemma-4-e4b-it
Google Gemma 4 E4B-IT is an open-source multimodal model with 8B total parameters and 4B effective parameters using selective parameter activation. It handles text and image input, generating text output, with a 256K context window and support for 140+ languages. Offers a good balance of performance and efficiency for deployment on consumer hardware.
Repository: localaiLicense: apache-2.0
arex-turbo
AREX-Turbo is BAAI's compact 4B deep-research agent, fine-tuned from
Qwen3.5-4B for long-horizon search, evidence aggregation, constraint
verification, and tool-assisted reasoning. It supports text and image
input with a 262K-token context window. This entry uses the recommended
Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
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